AI Code Mini-Scan
$499 one-time
Find the top AI attack paths in one private repo. Attack-path report, SARIF, Markdown, and validation plan. No subscription required.
SecEng Workbench · AI Attack-Path SAST
SecEng Code Scanner turns AI code risk into attack paths teams can fix.
Scan LLM apps, RAG workflows, MCP servers, browser agents, AI coding agents, tool-calling systems, and AI gateways for AI-native code risk. SecEng Code Scanner groups static signals into source-to-sink attack paths, produces safe validation plans, exports Jira/SARIF/VS Code evidence, and supports buyer or marketplace review without overstating exploitability.
WHERE CAN AI CODE BECOME AN ATTACK PATH?
AI-native SAST
Static signals grouped into attack paths instead of noisy line-by-line findings.
MCP / RAG / agents
Targets LLM apps, RAG workflows, browser agents, AI coding agents, and tool-calling systems.
SARIF / VS Code / Jira
Exports developer-ready diagnostics, tickets, and evidence with the language teams already use.
Marketplace-ready evidence
Supports AppExchange, AgentExchange, Splunkbase, GitHub, VS Code, and CI readiness workflows without claiming certification.
Early access
Licensed tooling plus Workbench-backed delivery for teams that need speed now.
SecEng Workbench · AI Attack-Path SAST
SecEng Code Scanner
Attack-path SAST
Static signals grouped into source → sink → missing-guard paths.
Attack-path evidence
Validation evidence for app review, pre-submission, and buyer security review.
CVE candidate triage
Rank likely research candidates without overstating exploitability.
Developer exports
SARIF, VS Code diagnostics, Jira tickets, Markdown, and control matrices.
Attack-path SAST
Static signals grouped into source → sink → missing-guard paths
Marketplace readiness
Evidence for app review, buyer review, and submission blockers
CVE candidate triage
Rank likely research candidates without overclaiming
SARIF / VS Code / Jira
Developer exports and remediation queues
Core capabilities
What SecEng Code Scanner does.
Target Profiling
Identify MCP servers, RAG systems, browser agents, AI coding agents, tool-calling apps, AI gateways, and model supply-chain surfaces before scanning deeper.
AST-lite Enrichment
Extract entrypoints, sources, sinks, guards, missing controls, framework signals, and path context from AI code.
Attack-path Grouping
Compress noisy static findings into prioritized AI attack paths: model/tool/source/sink/guard relationships that engineering teams can review and fix.
CVE-likelihood Scoring
Separate vulnerability candidates from commercial control gaps, test-only noise, and low-context static signals.
Safe Validation Planning
Generate mock-first harness plans for MCP tools, browser actions, RAG tenant boundaries, model JSON dispatch, prompt logging, and unsafe render paths.
Evidence Export
Export SARIF, VS Code diagnostics, Jira-ready tickets, disclosure case drafts, control matrices, and buyer evidence packages.
Evidence & signals
What you get out of the box.
Risk Classes
- MCP tool side effects without approval
- Browser-agent actions without domain or action policy
- RAG/vector retrieval without tenant boundaries
- Model-generated JSON controlling trusted actions
- Prompt, log, trace, and cache exposure before redaction
Outputs
- AI Code Attack-Path Report
- CVE Candidate Register
- Safe Validation Plan
- Marketplace Readiness Report
- Jira Tickets
- SARIF
Evidence Levels
- Raw signal
- AST-enriched finding
- Grouped attack path
- Validation planned
- Safe-dry-run validated
- Vendor/disclosure candidate
Red team + Blue team
Built for both sides of the security equation.
Red Team Use
- Seed adversarial testing with code-derived attack paths instead of waiting for runtime failures.
- Rank likely CVE candidates without pretending every static result is exploitable.
- Generate safe validation plans for marketplace, partner, and enterprise review blockers.
Blue Team Use
- Turn AI code findings into Jira-ready fixes, control gaps, and buyer evidence.
- Use SARIF, VS Code diagnostics, and markdown reports in the development workflow.
- Package marketplace-readiness artifacts without claiming official certification or replacing review.
Risk classes
AI-native risks classic SAST misses.
Code Scanner looks for the places where AI code creates delegated action, data exposure, unsafe rendering, policy bypass, or evidence gaps. The output is not a pile of raw static findings. It is a review path.
MCP tool side effects without approval
Browser-agent actions without domain or action policy
RAG/vector retrieval without tenant boundaries
Model-generated JSON controlling trusted actions
Prompt, log, trace, and cache exposure before redaction
Streaming model output rendered as unsafe HTML or markdown
AI gateway auth, budget, and model-policy gaps
Unsafe model artifact loading and unpinned supply-chain paths
Missing forensic evidence for AI actions
Outputs
What it produces.
The deliverable set is built for engineering, pre-submission validation, buyer security review, and disclosure triage when the evidence supports it.
AI Code Attack-Path Report
CVE Candidate Register
Safe Validation Plan
Harness Plan
Disclosure Case Draft
Developer Export & Remediation Evidence Pack
Pre-Submission Evidence Pack
Control Matrix
Jira Tickets
VS Code Diagnostics
SARIF
Differentiation
Not another noisy scanner.
Classic SAST emits findings. SecEng Code Scanner emits AI attack paths: where model output, tools, source data, sinks, and missing guards combine into reviewable risk.
Generic SAST
dangerous calls
SecEng Code Scanner
model/tool/source/sink/guard paths
Generic SAST
raw findings
SecEng Code Scanner
prioritized AI attack paths
Generic SAST
generic remediation
SecEng Code Scanner
Jira-ready fixes and control evidence
Generic SAST
pass/fail output
SecEng Code Scanner
validation plans, CVE candidates, and marketplace evidence
Evidence levels stay explicit
Static does not mean exploitable.
SecEng Code Scanner does not claim every static result is exploitable. Findings are labeled by evidence level so product, security, legal, and engineering teams know what can be said, what needs validation, and what belongs in a private remediation backlog.
CVE-likelihood scoring identifies candidates for private validation and pre-disclosure review. It does not constitute a CVE assignment, official vulnerability confirmation, or disclosure recommendation. Human review by a qualified practitioner is required before any submission to a CVE numbering authority or public disclosure program.
Pre-submission and review evidence
Prepare evidence before official review.
Use Code Scanner for pre-submission evidence packs, submission blocker reports, buyer security review artifacts, and remediation planning for AI-enabled apps, managed packages, partner apps, extensions, and integrations.
It supports pre-submission and validation workflows. It does not replace official Salesforce, Splunk, GitHub, AWS, partner, or marketplace security review.
Architecture
A meta-engine for AI code review evidence.
SecEng Code Scanner combines native AI security rules, source-context enrichment, path grouping, validation planning, and evidence export. It can also ingest customer-owned scanner output when the customer has the right to use it.
Import support does not mean SecEng bundles or resells third-party commercial tools or maintained rule sets.
Engine stages
- Native SecEng AI rulepacks
- AST-lite enrichment
- Target profiling
- Attack-path grouping
- CVE-likelihood scoring
- Harness planning
- Disclosure-case generation
- Buyer-evidence export
- SARIF/import adapters
Inputs it can consume
- native SecEng scan output
- SARIF
- customer-owned Semgrep JSON/SARIF output
- CodeQL, Snyk, or Sonar output where the customer has rights
- Salesforce Code Analyzer output where applicable
- Splunk AppInspect output where applicable
Commercial fit
Best first steps
Four entry points depending on scope, urgency, and whether you need tooling, an expert review, or both.
Entry
Mini-Scan
One codebase. CVE candidate register, safe validation plan, and developer export. First findings in hours.
Get startedRepeatable
Team License
Scanner access for a team. Recurring scanning, developer exports, and Jira backlog integration.
Get startedOEM
OEM Pilot
SecEng Scan as a white-label AI scanner module for your tool. SARIF-native output, no scanner rebuild required.
Get startedConsulting
Engagement Add-On
Scanner runs inside a Launch Security Review, red-team engagement, or product security assessment. Code risk to buyer-ready findings.
Get startedOEM and scanner-provider packaging
Your scanner covers the web. It doesn't cover AI-generated code, LLM apps, or agentic workflows.
SecEng Scan adds the AI layer. Packaged as a headless binary, localhost sidecar, private worker, or white-label module. Outputs in SARIF, JSON, Markdown, and evidence bundles that fit the existing scanner workflow — no shared code ownership, no scanner rebuild required.
- Customers are asking for AI security scanner coverage and your roadmap doesn't have room to build it
- Your DAST tool covers web and APIs but not AI-generated code, LLM application patterns, or agentic workflows
- A prospect's security questionnaire is asking about AI scanner coverage and you don't have an answer yet
- An MSSP's managed scanning service needs an AI module they can offer under their service brand
- Your scanner product team wants a SARIF-native AI findings feed without building the detection engine
SecEng Scan outputs are designed for scanner-native ingestion and human-reviewed triage. Findings flow into the partner's existing review workflow — not directly to end customers as automated claims.
Pricing & access
Start with tooling. Add expert review when the stakes are higher.
Use a license for repeatable scanning and developer exports, or scope a Workbench-backed review when a marketplace submission, enterprise buyer, or disclosure candidate needs human triage.
Request Mini-Scan
Starter
$199/mo
1 private repo, monthly scans, AI attack-path report, SARIF and Markdown exports.
Team
$599/mo
3 private repos, weekly scans, VS Code/SARIF/Jira exports, developer export and validation evidence pack, control matrix, and safe validation plans.
Review Pro
$1,299/mo
5 apps/repos/packages, pre-submission evidence packs, white-label evidence, CVE candidate triage, variant and patch-diff checks.
AI SECURITY ENGINEERING WORKBENCH
Ready to put SecEng Code Scanner to work?
Start with a licensed scan or scope a Workbench-backed review. We’ll identify AI-native code attack paths, rank the evidence, and produce the remediation and marketplace-readiness artifacts your team can actually use.
Product packet
Product detail pages now point into the same commercial library.
Workbench products can be sold through checkout, quotes, or scoped delivery while keeping the packet docs and entitlement path consistent.
Stripe-optimized packet
Mutual NDA
Mutual confidentiality protections for pre-sales, delivery, and research collaboration contexts.
Used for NDA baseline
Statement of Work Template
Mission-specific scope, deliverables, timeline, access, assumptions, and acceptance criteria for scoped AI security engagements.
Data Processing Addendum
Controller/processor allocation, data protection obligations, subprocessing, security measures, AI provider boundaries, and customer-data handling for scoped services.
Used for Data handling and public-safe claims
Publication & Claim-Readiness Policy
Claim-readiness criteria for public research, trust pages, scorecards, attestations, sponsor materials, security review outputs, and buyer-facing evidence.
Used for Data handling and public-safe claims · Sponsored research and launch
Payment terms
Direct checkout for self-serve tools; quote-first for service-backed deployments.
Acceptance terms
Accepted on delivery, activation, or fulfillment start depending on the product path.
Fulfillment workflow
product -> quote or checkout -> webhook -> entitlement -> launch room
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